Precise identification of medulloblastoma in MRI images using a convolutional neural network integrated with a self-attention mechanism.

Objective: Medulloblastoma (MB) is a highly malignant brain tumor. Early diagnosis and treatment are important to improve patients' survival. However, it is difficult to distinguish MB from other brain tumors in magnetic resonance imaging (MRI) with the naked eye. This study proposed a new hybrid deep learning model named InceptentionNet, combining Inception and self-attention mechanisms to recognize MB with MRI images. Methods: InceptentionNet integrated multiscale feature extraction and dynamic focus on relevant regions. This model was trained using a dataset with 736 MRI images, including 106 MB and 630 non-MB images. Other single convolutional neural network models, including MobileNet, Residual Network, Densely Connected Convolutional Network, Visual Geometry Group, and Inception, were also trained. All models' performance was evaluated. In addition, we conducted external tests to verify the generalization of the model. Results: The InceptentionNet model achieved an accuracy of 98.07% ± 0.77%, a precision of 91.43% ± 4.56%, a F1-score of 93.54% ± 2.44%. And the area under curve and recall were respectively 99.41% ± 0.08% and 96.03% ± 3.61%. In external tests, this model still performed best, achieving 90.94% accuracy and 92.79% AUC. These metrics indicated that our model exhibited a good performance in distinguishing MB. The accuracy of InceptentionNet was the highest among other single models, indicating our hybrid model outperform other models. Additionally, images combined with attention heatmaps exhibited high clinical interpretability. Conclusion: InceptentionNet demonstrates robust predictive capabilities and has the potential as a diagnostic assistant tool. In the future, the model should be trained and validated using larger data and multiclass classification should be expanded.